Summary of AVNET SMARTEDGE AGILE – END-TO-END IOT SOLUTION TO DELIVER AI AND SECURITY AT THE EDGE
The /SMARTEDGE AGILE meta-sensor and Brainium Machine Learning cloud platform form an AI-driven IoT ecosystem. Originally developed for sports performance analysis, the system uses edge computing to deploy complex machine learning models directly onto sensors. This architecture enables autonomous decision-making, reduces bandwidth needs, and ensures high security via TLS encryption, making AI implementation cost-effective and easy to configure.
Parts used in the SMARTEDGE AGILE Meta-Sensor System:
- /SMARTEDGE AGILE meta-sensor
- Brainium Machine Learning cloud platform
- Gateway devices for network connection
- Edge-to-cloud security infrastructure
The /SMARTEDGE AGILE meta-sensor, together with the Brainium Machine Learning cloud platform, form part of an ecosystem which provides a full, machine learning IoT system. The key advantages of this system are the use of Artificial Intelligence to analyse and monitor complex behaviours in motion, or of any of the other parameters for which sensor types are built into the AGILE meta-sensor. Brainium builds a complex Machine Learning model from the raw data acquired by AGILE. That model is then deployed right back to AGILE at the edge of the IoT network;
now, real AI is running at the edge, where only qualiBrainium, and the /SMARTEDGE AGILE meta-sensor’s origins lie in developments in the sports industry, where analysing motion is critically important to top sporting performance. A highly skilled software development team has built a comparatively easy to use platform, consisting of deceptively complex machine learning systems, and autonomous meta-sensors which together, deliver ease of use, and a complete configuration platform. At its heart is over four years of real machine learning. An IoT network consisting of the Brainium platform and AGILE sensors is easy to put together because of its edge-to-cloud architecture. Any gateway used by the AGILE meta-sensors to connect to the Brainium platform is completely transparent to AGILE data traffic due to its edge-to-cloud security, making deployment easy. Further, the Brainium platform itself can be consumed as a Cloud service, or it can be containerised for use within other services, or even run privately. Best in class network security has been built in from the start, with full edge-to-cloud certificate based TLS security implemented as standard, protecting you, your network, and your reputation from attack. AI at the Edge increases the immediacy of decision making, and lessens the need for high volumes of traffic, reducing the need for high bandwidth connections from Edge to Cloud. All of this means a less expensive ongoing cost of ownership. In short Brainium and the Agile meta-sensor device make AI easy to implement, and cost effective to deploy without compromising on security.fied data is now sent to the Cloud, and decisions can be taken autonomously without the need for constant involvement by Cloud service.
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- How does the system handle data processing?
The system builds complex machine learning models from raw sensor data and deploys them back to the AGILE device at the edge of the IoT network. - Can the Brainium platform be deployed privately?
Yes, the platform can be consumed as a Cloud service, containerised for other services, or run privately. - What security protocol is implemented by default?
Full edge-to-cloud certificate based TLS security is implemented as standard to protect networks and data. - Does the system require high bandwidth connections?
No, running AI at the edge increases immediacy and lessens the need for high volumes of traffic, reducing bandwidth requirements. - Where did the technology originate?
The origins lie in developments in the sports industry where analysing motion is critically important. - Is the deployment process complicated?
No, any gateway used is completely transparent to data traffic, making deployment easy due to the edge-to-cloud architecture. - How does the system reduce costs?
Reduced bandwidth needs and autonomous decision-making lead to a less expensive ongoing cost of ownership. - What makes the platform easy to use?
A skilled software team built a comparatively easy to use platform consisting of autonomous meta-sensors and configuration tools.
